I will discuss new methods and studies that aim to improve eyes-free data entry for blind mobile device users. Currently, mobile devices are generally accessible to blind people, but text entry is almost prohibitively slow. Studies show that blind people enter text on an iPhone at a rate of just 4 words per minute. I will present Perkinput, a chording text entry method where users touch the screen with one to three fingers at a time in patterns based on Braille. Instead of soft keys, Perkinput uses concepts from signal detection theory to determine the user’s input.
In this talk, I will describe computational tools I helped develop for
analyzing and manipulating the backbone of macromolecular 3D structures, and
I demonstrate that these tools support building better macromolecular
structures than current methodology.
Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling non-idealities in data, such as imputation and expectation-maximization, are often difficult to analyze theoretically and/or terminate in local optima of non-convex functions -- these problems are only exacerbated in high-dimensional settings. We present new methods for obtaining high-dimensional regression estimators in the presence of corrupted data, and provide theoretical guarantees for the statistical consistency of our methods.